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July IP Litigation Newsletter

The Cloakroom with Peter Roskam

The Cloakroom with Peter Roskam: 2024 Republican National Convention

Elise Elam Moderates Panel at ACLI Compliance and Legal Sections Annual Meeting

James Sherer Speaks on Panel at The Masters Conference

<img decoding="async" loading="lazy" class="alignright wp-image-6624" src="https://www.bakerdatacounsel.com/wp-content/uploads/sites/5/2021/05/GettyImages-870184586_1200x630.jpg" alt="" width="438" height="230" />
Our last AI post on this blog, the <a href="https://www.bakerdatacounsel.com/ai/the-new-if-decidedly-not-final-frontier-of-artificial-intelligence-regulation/">New (if Decidedly Not ‘Final’) Frontier of Artificial Intelligence Regulation</a>, touched on both the Federal Trade Commission’s (FTC) <a href="https://www.ftc.gov/news-events/blogs/business-blog/2021/04/aiming-truth-fairness-equity-your-companys-use-ai">April 19, 2021, AI guidance</a> and the European Commission’s <a href="https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-european-approach-artificial-intelligence">proposed AI Regulation</a>. The FTC’s 2021 guidance referenced, in large part, the FTC’s April 2020 post “<a href="https://www.ftc.gov/comment/reply/1570535#comment-form">Using Artificial Intelligence and Algorithms</a>.” The recent FTC guidance also relied on older FTC work on AI, including a January 2016 report, “<a href="https://www.ftc.gov/system/files/documents/reports/big-data-tool-inclusion-or-exclusion-understanding-issues/160106big-data-rpt.pdf">Big Data: A Tool for Inclusion or Exclusion</a>?,” which in turn followed a September 15, 2014, workshop on the same topic. The Big Data workshop addressed data modeling, data mining and analytics, and gave us a prospective look at what would become an FTC strategy on AI.
The FTC’s guidance begins with the data, and the 2016 guidance on big data and subsequent AI development addresses this most directly. The 2020 guidance then highlights important principles such as transparency, explain-ability, fairness, accuracy and accountability for organizations to consider. And the 2021 guidance elaborates on how consent, or opt-in, mechanisms work when an organization is gathering the data used for model development.
Taken together, the three sets of FTC guidance — the 2021, 2020, and 2016 guidance ― provide insight into the FTC’s approach to organizational use of AI, which spans a vast portion of the data life cycle, including the creation, refinement, use and back-end auditing of AI. As a whole, the various pieces of FTC guidance also provide a multistep process for what the FTC appears to view as responsible AI use. In this post, we summarize our takeaways from the FTC’s AI guidance across the data life cycle to provide a practical approach to responsible AI deployment.
<img decoding="async" loading="lazy" class=" wp-image-6626 alignleft" src="https://www.bakerdatacounsel.com/wp-content/uploads/sites/5/2021/05/Graphic-Data-Counsel.jpg" alt="" width="204" height="1448" />Data Gathering
– Evaluation of a data set should assess the quality of the data (including accuracy, completeness and representativeness) ― and if the data set is missing certain population data, the organization must take appropriate steps to address and remedy that issue (2016).
– An organization must honor promises made to consumers and provide consumers with substantive information about the organization’s data practices when gathering information for AI purposes (2016). Any related opt-in mechanisms for such data gathering must operate as disclosed to consumers (2021).
Data Compilation
– An organization should recognize the data compilation step as a “descriptive activity,” which the FTC defines as a process aimed at uncovering and summarizing “patterns or features that exist in data sets” — a reference to <a href="http://infolab.stanford.edu/~ullman/mmds.html">data mining scholarship</a> (2016) (note that the FTC’s referenced materials originally at mmds.org are now redirected).
– Compilation efforts should be organized around a life cycle model that provides for compilation and consolidation before moving on to data mining, analytics and use (2016).
– An organization must recognize that there may be uncorrected biases in underlying consumer data that will surface in a compilation; therefore, an organization should review data sets to ensure hidden biases are not creating unintended discriminatory impacts (2016).
– An organization should maintain reasonable security over consumer data (2016).
– If data are collected from individuals in a deceitful or otherwise inappropriate manner, the organization may need to delete the data (2021).
Model and AI Application Selection
– An organization should recognize the model and AI application selection step as a predictive activity, where an organization is using “statistical models to generate new data” — a reference to <a href="http://www.stat.berkeley.edu/~aldous/157/Papers/shmueli.pdf">predictive analytics scholarship</a> (2016).
– An organization must determine if a proposed data model or application properly accounts for biases (2016). Where there are shortcomings in the data model, the model’s use must be accordingly limited (2021).
– Organizations that build AI models may “not sell their big data analytics products to customers if they know or have reason to know that those customers will use the products for fraudulent or discriminatory purposes.” An organization must, therefore, evaluate potential limitations on the provision or use of AI applications to ensure there is a “permissible purpose” for the use of the application (2016).
– Finally, as a general rule, the FTC asserts that under the FTC Act, a practice is patently unfair if it causes more harm than good (2021).
Model Development
– Organizations must design models to account for data gaps (2021).
– Organizations must consider whether their reliance on particular AI models raises ethical or fairness concerns (2016).
– Organizations must consider the end uses of the models and cannot create, market or sell “insights” used for fraudulent or discriminatory purposes (2016).
Model Testing and Refinement
– Organizations must test the algorithm before use (2021). This testing should include an evaluation of AI outcomes (2020).
– Organizations must consider prediction accuracy when using “big data” (2016).
– Model evaluation must focus on both inputs and AI models may not discriminate against a protected class (2020).
– Input evaluation should include considerations of ethnically based factors or proxies for such factors.
– Outcome evaluation is critical for all models, including facially neutral models.
– Model evaluation should consider alternative models, as the FTC can challenge models if a less discriminatory alternative would achieve the same results (2020).
– If data are collected from individuals in a deceptive, unfair, or illegal manner, deletion of any AI models or algorithms developed from the data may also be required (2021).
Front-End Consumer and User Disclosures
– Organizations must be transparent and not mislead consumers “about the nature of the interaction” ― and not utilize fake “engager profiles” as part of their AI services (2020).
– Organizations cannot exaggerate an AI model’s efficacy or misinform consumers about whether AI results are fair or unbiased. According to the FTC, deceptive AI statements are actionable (2021).
– If algorithms are used to assign scores to consumers, an organization must disclose key factors that affect the score, rank-ordered according to importance (2020).
– Organizations providing certain types of reports through AI services must also provide notices to the users of such reports (2016).
– Organizations building AI models based on consumer data must, at least in some circumstances, allow consumers access to the information supporting the AI models (2016).
Back-End Consumer and User Disclosures
– Automated decisions based on third-party data may require the organization using the third-party data to provide the consumer with an “adverse action” notice (for example, if under the <a href="https://www.ftc.gov/system/files/documents/statutes/fair-credit-reporting-act/545a_fair-credit-reporting-act-0918.pdf">Fair Credit Reporting Act 15 U.S.C. § 1681 (Rev. Sept. 2018)</a>, such decisions deny an applicant an apartment or charge them a higher rent) (2020).
– General “you don’t meet our criteria” disclosures are not sufficient. The FTC expects end users to know what specific data are used in the AI model and how the data are used by the AI model to make a decision (2020).
– Organizations that change specific terms of deals based on automated systems must disclose the changes and reasoning to consumers (2020).
– Organizations should provide consumers with an opportunity to amend or supplement information used to make decisions about them (2020) and allow consumers to correct errors or inaccuracies in their personal information (2016).
Model Deployment
– When deploying models, organizations must confirm that the AI models have been validated to ensure they work as intended and do not illegally discriminate (2020).
– Organizations must carefully evaluate and select an appropriate AI accountability mechanism, transparency framework and/or independent standard, and implement as applicable (2020).
– An organization should determine the fairness of an AI model by examining whether the particular model causes, or is likely to cause, substantial harm to consumers that is not reasonably avoidable and not outweighed by countervailing benefits (2021).
Model Audit
– Organizations must test AI models periodically to revalidate that they function as intended (2020) and to ensure a lack of discriminatory effects (2021).
– Organizations must account for compliance, ethics, fairness and equality when using AI models, taking into account four key questions (2016; 2020):
– How representative is the data set? 
– Does the AI model account for biases? 
– How accurate are the AI predictions? 
– Does the reliance on the data set raise ethical or fairness concerns?
– Organizations must embrace transparency and independence, which can be achieved in part through the following (2021):
– Using independent, third-party audit processes and auditors, which are immune to the intent of the AI model. 
– Ensuring data sets and AI source code are open to external inspection. 
– Applying appropriate recognized AI transparency frameworks, accountability mechanisms and independent standards. 
– Publishing the results of third-party AI audits.
– Organizations remain accountable throughout the AI data life cycle under the FTC’s recommendations for AI transparency and independence (2021).

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Digital Assets and Data Management

The Not-So-Hidden FTC Guidance on Organizational Use of Artificial Intelligence (AI), from Data Gathering Through Model Audits

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